A virtual machine scheduler based on CPU and I/O-bound features for energy-aware in high performance computing clouds. (November 2016)

• Record Type: Journal Article
• Authors: Fernandes, Felipe; Beserra, David; Moreno, Edward David; Schulze, Bruno; Pinto, Raquel Coelho Gomes
• Is Part Of: Computers & electrical engineering. Volume 56(2016)
• Page Start: 854
• Page End: 870
• Publication Date: 2016-11
• Subjects: Green cloud computing -- HPC -- VM scheduler -- Energy-aware; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.09.003
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Our method imply in energy saving due overlap different VM workloads (CPU and I/O). Combine different VM types in the same host does not impact VM performance. We focused in observe the ration between performance and energy consumption. Allocation policy differing VM types, minimizes VM migration and SLA violations. Graphical abstract: Abstract: A Virtual Machine (VM) scheduler for homogeneous High Performance Computing (HPC) cloud environments is presented in this paper. This scheduler considers each VM workload type (CPU or I/O-bound) to decide on its allocation. Scheduler is able to reduce energy consumption, as well as SLA violations on this cloud environment, avoiding performance losses by allocating simultaneously VMs which run different types of tasks. The scheduler method was validated through simulations conducted with the CloudSim framework. Two synthetic benchmarks representing both workload types were run previously in VMs in order to obtain basic data to design the scheduler. Thus, it was possible to implement a scheduling method that employs a VM allocation policy based on features of each application. Results showed that knowing the specifications and characteristics of environment may contribute to a better usage of resources, leading to an increased level of services availability and, finally, reducing problems caused by competition in resource usage.